| On Riemannian optimization over positive definite matrices with the Bures-Wasserstein geometry A Han, B Mishra, PK Jawanpuria, J Gao Advances in Neural Information Processing Systems 34, 8940-8953, 2021 | 32 | 2021 |
| Improved variance reduction methods for Riemannian non-convex optimization A Han, J Gao IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (11), 7610 …, 2021 | 19* | 2021 |
| Riemannian Hamiltonian methods for min-max optimization on manifolds A Han, B Mishra, P Jawanpuria, P Kumar, J Gao SIAM Journal on Optimization 33 (3), 1797-1827, 2023 | 15 | 2023 |
| A simple yet effective framelet-based graph neural network for directed graphs C Zou, A Han, L Lin, M Li, J Gao IEEE Transactions on Artificial Intelligence, 2023 | 13* | 2023 |
| Riemannian stochastic recursive momentum method for non-convex optimization A Han, J Gao International Joint Conference on Artificial Intelligence, 2505-2511, 2021 | 10 | 2021 |
| Generalized energy and gradient flow via graph framelets A Han, D Shi, Z Shao, J Gao arXiv preprint arXiv:2210.04124, 2022 | 9 | 2022 |
| Differentially private Riemannian optimization A Han, B Mishra, P Jawanpuria, J Gao Machine Learning 113 (3), 1133-1161, 2024 | 8 | 2024 |
| From continuous dynamics to graph neural networks: Neural diffusion and beyond A Han, D Shi, L Lin, J Gao arXiv preprint arXiv:2310.10121, 2023 | 7 | 2023 |
| Learning with symmetric positive definite matrices via generalized Bures-Wasserstein geometry A Han, B Mishra, P Jawanpuria, J Gao International Conference on Geometric Science of Information, 405-415, 2023 | 7* | 2023 |
| Riemannian accelerated gradient methods via extrapolation A Han, B Mishra, P Jawanpuria, J Gao International Conference on Artificial Intelligence and Statistics, 1554-1585, 2023 | 7 | 2023 |
| Riemannian block SPD coupling manifold and its application to optimal transport A Han, B Mishra, P Jawanpuria, J Gao Machine Learning 113 (4), 1595-1622, 2024 | 6 | 2024 |
| Unifying over-smoothing and over-squashing in graph neural networks: A physics informed approach and beyond Z Shao, D Shi, A Han, Y Guo, Q Zhao, J Gao arXiv preprint arXiv:2309.02769, 2023 | 6 | 2023 |
| Enhancing framelet GCNs with generalized p-Laplacian regularization Z Shao, D Shi, A Han, A Vasnev, Y Guo, J Gao International Journal of Machine Learning and Cybernetics 15 (4), 1553-1573, 2024 | 5* | 2024 |
| Rieoptax: Riemannian Optimization in JAX S Utpala, A Han, P Jawanpuria, B Mishra arXiv preprint arXiv:2210.04840, 2022 | 5 | 2022 |
| Escape saddle points faster on manifolds via perturbed riemannian stochastic recursive gradient A Han, J Gao arXiv preprint arXiv:2010.12191, 2020 | 4 | 2020 |
| Design your own universe: A physics-informed agnostic method for enhancing graph neural networks D Shi, A Han, L Lin, Y Guo, Z Wang, J Gao arXiv preprint arXiv:2401.14580, 2024 | 3 | 2024 |
| Exposition on over-squashing problem on GNNs: Current Methods, Benchmarks and Challenges D Shi, A Han, L Lin, Y Guo, J Gao arXiv preprint arXiv:2311.07073, 2023 | 3 | 2023 |
| Improved differentially private Riemannian optimization: Fast sampling and variance reduction S Utpala, A Han, P Jawanpuria, B Mishra Transactions on Machine Learning Research, 2023 | 3 | 2023 |
| Nonconvex-nonconcave min-max optimization on Riemannian manifolds A Han, B Mishra, P Jawanpuria, J Gao Transactions on Machine Learning Research, 2023 | 3 | 2023 |
| A framework for bilevel optimization on Riemannian manifolds A Han, B Mishra, P Jawanpuria, A Takeda arXiv preprint arXiv:2402.03883, 2024 | 2 | 2024 |
Professor Junbin GaoDiscipline of Business Analytics in Business School, The University of SydneyVerified email at sydney.edu.au
